Data Processing

Editing Coding Classification Tabulation Analysis

Editing of Data After the data is edited, it becomes information If data is not edited, it would take more time and would be difficult for the researcher to arrive at results Meaning Checking or testing of the importance of data Removing unnecessary unrequired information To provide accurate, complete and consistent info.

Editing of Data Micro and Macro Level Editing Field and Central Editing Objectives to ensure Accuracy of data Consistency of data Completeness of data Coherence of aggregated data The best possible data

Editing of Data

Coding of Data Process of assigning figures or symbols to answers Responses to be put into a number of categories Characteristics Exhaustiveness Mutually Exclusive Unidimensionality Netting or bucketing What do you like best in BMS? Questionnaires could be precoded

Classification of Data Data classified into homogenous groups to get meaningful data Grouping of data into common groups or classes based on common characteristics Example: Data being classified as per target groups requested by clients

Classification of Data Types Qualitative (Gender, Religions) Quantitative (number of members in family, pets, TV sets) Geographical area Class Interval (ranges: age, income)

Tabulation Process of summarizing raw data and displaying it in a compact form for analysis Orderly arrangement of data in a logical order While tabulation on the computer the data is converted to a numeric form Binary data sheets made

Tabulation 64 36 70 30

Definitely Would not buy Tabulation Question: Would you buy this product? General Population (458) TG1 (156) TG2 (84) Definitely would buy 28.6 19.2 8.6 Probably Would buy 34.5 50.3 5.7 May or May not buy 9.4 20.1 16.3 Probably would not buy 22.8 6.4 35.7 Definitely Would not buy 4.7 4 33.7

Importance of Tabulation Facilitates process of comparison Preserves space and reduces explanatory and descriptive statements at minimum Helps to detect errors and omissions Identity to data Helps simplify complex data Basis for statistical processing

Data Analysis Descriptive Analysis Inferential Analysis Unidimensional Analysis Bivariate Analysis (Correlation and Causal) Multivariate Analysis Inferential Analysis Statistical confidence Determine validity of data with conclusion

Data Interpretation Converting the small stories to bigger ones Explanations, reasons, causes can be provided Interpretations Generalisations Hypotheses testing Applying extraneous information Expert Opinions